Actuatable occupant restraining systems having an inflatable air bag in vehicles are known in the art. Such systems that are controlled in response to whether the seat is empty or occupied, whether a rearward facing child seat (“RFCS”) is present on the seat, and/or in response to the occupant's position, weight, size, etc., are referred to as smart restraining systems. One example of a smart actuatable restraining system is disclosed in U.S. Pat. No. 5,330,226.
Pattern recognition systems may be defined as systems capable of distinguishing between classes of real world stimuli according to a plurality of distinguishing characteristics, or features, associated with the classes. A number of pattern recognition systems that use vision system inputs are known in the art, including statistical classifiers such as neural networks and support vector machines. Support vector machines are described by Vladimir Vapnik [C. Cortes and V. Vapnik, in “Support Vector Networks,” Machine Learning, Vol. 20, pp. 273-97, 1995].
Support vector machines are intelligent systems that generate appropriate separating functions for a plurality of output classes from a set of training data. The separating functions divide an N-dimensional feature space into portions associated with the respective output classes, where each dimension is defined by a feature used for classification. Once the separators have been established, future input to the system can be classified according to its location in feature space (e.g., its value for N features) relative to the separators. In its simplest form, a support vector machine distinguishes between two output classes, a “positive” class and a “negative” class, with the feature space segmented by the separators into regions representing the two alternatives.